Join us on Friday, April 9 for a 12-1pm talk by Beenish Chaudhry entitled: “Putting Healthcare in Your Hands” in King 235.

Abstract: Our healthcare system is undergoing a seismic shift that is breaking the old information asymmetry – where doctors had all the information and patients had very little – now technology is being used to collect data to give patients an easy way to access key facts and gain knowledge for decisions about health. While we know how to target this information for the affluent and their lifestyles, what are we doing for those with low socioeconomic background? In this talk, you will learn about technology design challenges and solutions for this population, and what needs to be done in the future.

Mark Allman, International Computer Science Institute will give a talk. The Domain Name System (DNS) is a crucial piece of the Internet’s fabric, charged with mapping human-friendly names into network addresses. This talk highlights several recent projects that aim to explore and understand how the modern DNS ecosystem has organically developed. We will first tackle the complexity of the system and then illustrate how that complexity causes potential security vulnerabilities. Finally, we will briefly sketch several possible mitigations to these security issues– which is the subject of ongoing work.

On Thursday, November 14 there will be a talk by Dr. Matt Kretchmar of Denison University entitled Automated Identification of Text Message Authors: Was that really you who sent that text message?

Reception with light refreshments at 4:00pm in King 225, talk to follow at 4:30pm in King 239

Abstract: This talk is about the application of machine learning techniques to the problem of classifying authors of text messages. We use kernel-based support vector machines to build an automated classifier that uses statistical idiosyncrasies to distinguish one sender from others.

The talk is aimed at general undergraduate students in both mathematics and computer science.

Opportunities for Machine Learning in Ecological Science and Ecosystem Management
How can computer science address the many challenges of managing the
earth's ecosystems sustainably? Viewed as a control problem, ecosystem
management is challenging for two reasons. First, we lack good models
of the function and structure of the earth's ecosystems.
Second, it is difficult to compute optimal management policies because ecosystems
exhibit complex spatio-temporal interactions at multiple scales.
This talk will discuss some of the many challenges and opportunities
for machine learning research in computational sustainability. These
include sensor placement, data interpretation, model fitting,
computing robust optimal policies, and finally executing those
policies successfully. I'll provide examples from current work
and discuss open problems in each of these areas.
All of these sustainability problems involve spatial modeling and
optimization, and all of them can be usefully conceived in terms of
facilitating or preventing flows along edges in spatial networks. For
example, encouraging the recovery of endangered species involves
creating a network of suitable habitat and encouraging spread along
the edges of the network. Conversely, preventing the spread of
diseases, invasive species, and pollutants involves preventing flow
along edges of networks. Addressing these problems will require
advances in several areas of machine learning and optimization.

Disabling the MacBook Webcam Indicator LED Modern computers contain a surprising number of processors distinct from the CPUs, each dedicated to a specific task. These processors along with their perhipherals form embedded systems inside standard desktop and laptop systems which are frequently overlooked when evaluating the security of computer systems. In this talk, I’ll describe a security analysis of one such embeddedsystem: the Apple iSight webcam. The iSight contains, as a privacy feature, an indicator LED which provides a visual cue that the camera is turned on. I’ll describe how the hardware that controls the LED can be bypassed, enabling video to be captured without any indication to the user. I’ll also show how the iSight can be leveraged by malware to break out of a Virtual Machine sandbox.

Stephen Checkoway, is an Assistant Research Professor in the Johns
Hopkins University Department of Computer Science and a member of the
Johns Hopkins University Information Security Institute where he
teaches courses on computer security and software vulnerabilities. His
work includes security analyses of automotive emedded systems and
computer voting systems as well as offensive and defensive computer
security research. Checkoway earned bachelor’s degrees in mathematics
and computer sciences from the University of Washington in 2005 and a
Ph.D. in computer science in 2012 from the University of California,
San Diego.